It comes from my concrete example: The probability that there is a goal is 60% so the probability that there is no goal is 1 - 60% which is 40%. I should have been more clear here!
I think this MIT course is a very comprehensive overview if you want to dive very deep: ocw.mit.edu/courses/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/
60% x 60% x 60% = 21.6%.((0.6 x 0.6 x 0.6) x 100) This shows that the probability of scoring three goals at the end of 90 minutes is 21.6%. 60% is the probability of scoring a goal after 30 minutes so the probability a team will score a goal in every 30 minutes of the game would be 21.6%
Hi, thanks for video, just subscribed. I am confused at one point @ 4:05. Is there a law that states on average you will be at the same point on time "s" as you were when on time "u"? Since I am assuming this is heavily dependent on the volatility and would be averaged with multiple other probability simulations, is it just the safe general assumption that there will be little to no change in the price?
@@syzygy9465 the fact that increments follow a normal distribution with mean 0 is why he stated that. and the symmetry of the normal distribution is why the probabilities of going up and down are equal. a high standard deviation means more likeliness of big(positive and negative) increments, but it doesn't change the fact that the mean is still zero.
It simply means there could be multiple path ( results) like in football match.4*.4*.4(no goal)or .6*.6*.6(3 goal) and so on.. And all representation sums upto 1.(exhaustive)
This is brilliant, thanks for the explanation
Thank you for these videos! Could you please explain where does 40% come from @ 2:12?
It comes from my concrete example: The probability that there is a goal is 60% so the probability that there is no goal is 1 - 60% which is 40%. I should have been more clear here!
I had the same queation
Thanks very good explaination!!
Beautifully explained ! I'm glad to find out this video.
May I ask which books would you recommend to study this subject ?
I think this MIT course is a very comprehensive overview if you want to dive very deep: ocw.mit.edu/courses/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/
Hi, thank you for this. Just confused about how you got 21.6% at 1:40. Can you explain that, please? Thank you
60% x 60% x 60% = 21.6%.((0.6 x 0.6 x 0.6) x 100) This shows that the probability of scoring three goals at the end of 90 minutes is 21.6%. 60% is the probability of scoring a goal after 30 minutes so the probability a team will score a goal in every 30 minutes of the game would be 21.6%
thank you@@maxwellclarke7320
Hi, thanks for video, just subscribed. I am confused at one point @ 4:05.
Is there a law that states on average you will be at the same point on time "s" as you were when on time "u"? Since I am assuming this is heavily dependent on the volatility and would be averaged with multiple other probability simulations, is it just the safe general assumption that there will be little to no change in the price?
Is this when sigma (volatility of the volatility) becomes significant? (learning about Heston Model applications btw)
Sorry I do not quite understand your question. I just defined s and u to be two different points in time. Could you maybe rephrase?
@@syzygy9465 the fact that increments follow a normal distribution with mean 0 is why he stated that. and the symmetry of the normal distribution is why the probabilities of going up and down are equal. a high standard deviation means more likeliness of big(positive and negative) increments, but it doesn't change the fact that the mean is still zero.
It simply means there could be multiple path ( results) like in football match.4*.4*.4(no goal)or .6*.6*.6(3 goal) and so on..
And all representation sums upto 1.(exhaustive)
amazing, amazing, amazing
Not good.